Denoising of volumetric magnetic resonance imaging using multi-channel three-dimensional convolutional neural network with applications on fast spin echo acquisitions

Three-dimensional (3D) magnetic resonance imaging (MRI) can be acquired with a high spatial resolution with flexibility being reformatted into arbitrary planes, but at the cost of reduced signal-to-noise ratio. Deep-learning methods are promising for denoising in MRI. However, the existing 3D denoising convolutional neural networks (CNNs) rely on either a multi-channel two-dimensional (2D) network or a single-channel 3D network with limited ability to extract high dimensional features. We aim to develop a deep learning approach based on multi-channel 3D convolution to utilize inherent noise information embedded in multiple number of excitation (NEX) acquisition for denoising 3D fast spin echo (FSE) MRI.